{
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"
    }
   },
   "cell_type": "markdown",
   "id": "4e16130e-e403-423b-9254-dba16851a072",
   "metadata": {},
   "source": [
    "![20191004161740356.png](attachment:b85cf6a8-da9e-4745-8e79-f7994a9815c3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b58f6e",
   "metadata": {},
   "source": [
    "## SKconv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35567fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "#from thop import profile\n",
    "#from thop import clever_format\n",
    "\n",
    "class SKConv(nn.Module):\n",
    "    def __init__(self, features, M=2, G=32, r=16, stride=1 ,L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            features: input channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            stride: stride, default 1.\n",
    "            L: the minimum dim of the vector z in paper, default 32.\n",
    "        \"\"\"\n",
    "        super(SKConv, self).__init__()\n",
    "        d = max(int(features/r), L)\n",
    "        self.M = M\n",
    "        self.features = features\n",
    "        self.convs = nn.ModuleList([])\n",
    "        # M 使用几个卷积核去卷积\n",
    "        for i in range(M):\n",
    "            self.convs.append(nn.Sequential(\n",
    "                nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False),\n",
    "                nn.BatchNorm2d(features),\n",
    "                nn.ReLU(inplace=True)\n",
    "            ))\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1,1))\n",
    "        self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False),\n",
    "                                nn.BatchNorm2d(d),\n",
    "                                nn.ReLU(inplace=True))\n",
    "        self.fcs = nn.ModuleList([])\n",
    "        # 每个卷积核，卷积后结果都需要全连接\n",
    "        for i in range(M):\n",
    "            self.fcs.append(\n",
    "                 nn.Conv2d(d, features, kernel_size=1, stride=1)\n",
    "            )\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        \n",
    "        feats = [conv(x) for conv in self.convs]\n",
    "        #进行拼接\n",
    "        feats = torch.cat(feats, dim=1)\n",
    "        feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])\n",
    "        #U\n",
    "        feats_U = torch.sum(feats, dim=1)\n",
    "        #sc\n",
    "        feats_S = self.gap(feats_U)\n",
    "        #z\n",
    "        feats_Z = self.fc(feats_S)\n",
    "\n",
    "        #attention\n",
    "        attention_vectors = [fc(feats_Z) for fc in self.fcs]\n",
    "        attention_vectors = torch.cat(attention_vectors, dim=1)\n",
    "        attention_vectors = attention_vectors.view(batch_size, self.M, self.features, 1, 1)\n",
    "        #attention 的权重\n",
    "        attention_vectors = self.softmax(attention_vectors)\n",
    "        \n",
    "        feats_V = torch.sum(feats*attention_vectors, dim=1)\n",
    "        \n",
    "        return feats_V"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ab72eb",
   "metadata": {},
   "source": [
    "## SKunit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f65ec506",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SKUnit(nn.Module):\n",
    "    def __init__(self, in_features, mid_features, out_features, M=2, G=32, r=16, stride=1, L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            in_features: input channel dimensionality.\n",
    "            out_features: output channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n",
    "            stride: stride.\n",
    "            L: the minimum dim of the vector z in paper.\n",
    "        \"\"\"\n",
    "        super(SKUnit, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_features, mid_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(mid_features),\n",
    "            nn.ReLU(inplace=True)\n",
    "            )\n",
    "        \n",
    "        self.conv2_sk = SKConv(mid_features, M=M, G=G, r=r, stride=stride, L=L)\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(mid_features, out_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "\n",
    "        if in_features == out_features: # when dim not change, input_features could be added diectly to out\n",
    "            self.shortcut = nn.Sequential()\n",
    "        else: # when dim not change, input_features should also change dim to be added to out\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_features, out_features, 1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        \n",
    "        out = self.conv1(x)\n",
    "        out = self.conv2_sk(out)\n",
    "        out = self.conv3(out)\n",
    "        \n",
    "        return self.relu(out + self.shortcut(residual))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d23e6a",
   "metadata": {},
   "source": [
    "## SKnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c07efcb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class SKNet(nn.Module):\n",
    "    def __init__(self, class_num, nums_block_list = [3, 4, 6, 3], strides_list = [1, 2, 2, 2]):\n",
    "        super(SKNet, self).__init__()\n",
    "        self.basic_conv = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        self.maxpool = nn.MaxPool2d(3,2,1)\n",
    "        \n",
    "        self.stage_1 = self._make_layer(64, 128, 256, nums_block=nums_block_list[0], stride=strides_list[0])\n",
    "        self.stage_2 = self._make_layer(256, 256, 512, nums_block=nums_block_list[1], stride=strides_list[1])\n",
    "        self.stage_3 = self._make_layer(512, 512, 1024, nums_block=nums_block_list[2], stride=strides_list[2])\n",
    "        self.stage_4 = self._make_layer(1024, 1024, 2048, nums_block=nums_block_list[3], stride=strides_list[3])\n",
    "     \n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.classifier = nn.Linear(2048, class_num)\n",
    "        \n",
    "    def _make_layer(self, in_feats, mid_feats, out_feats, nums_block, stride=1):\n",
    "        layers=[SKUnit(in_feats, mid_feats, out_feats, stride=stride)]\n",
    "        for _ in range(1,nums_block):\n",
    "            layers.append(SKUnit(out_feats, mid_feats, out_feats))\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        fea = self.basic_conv(x)\n",
    "        fea = self.maxpool(fea)\n",
    "        fea = self.stage_1(fea)\n",
    "        fea = self.stage_2(fea)\n",
    "        fea = self.stage_3(fea)\n",
    "        fea = self.stage_4(fea)\n",
    "        fea = self.gap(fea)\n",
    "        fea = torch.squeeze(fea)\n",
    "        fea = self.classifier(fea)\n",
    "        return fea\n",
    "    \n",
    "def SKNet26(nums_class=100):\n",
    "    return SKNet(nums_class, [1, 1, 2, 1])\n",
    "def SKNet50(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 6, 3])\n",
    "def SKNet101(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 23, 3])\n",
    "\n",
    "## test\n",
    "#x = torch.rand(8, 3, 224, 224)\n",
    "net = SKNet26()\n",
    "#out = model(x)\n",
    "#print('out shape : {}'.format(out.shape))\n",
    "#如果有gpu就使用gpu，否则使用cpu\n",
    "device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')\n",
    "net = net.to(device)\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdd9344",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f1a529d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#加载数据集\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5071, 0.4865, 0.4409], std=[0.2673, 0.2564, 0.2762])\n",
    "])\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5071, 0.4865, 0.4409], std=[0.2673, 0.2564, 0.2762])\n",
    "])\n",
    "\n",
    "train_dataset = torchvision.datasets.cifar.CIFAR100(root='./data/cifar100', train=True, transform=transform_train, download=True)\n",
    "test_dataset = torchvision.datasets.cifar.CIFAR100(root='./data/cifar100', train=False, transform=transform_test, download=True)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=256, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46fc007a",
   "metadata": {},
   "source": [
    "## train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d870209d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#超参数设置\n",
    "epochs = 30\n",
    "BATCH_SIZE = 128\n",
    "LR = 0.01\n",
    "\n",
    "#定义损失函数和优化器\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4)\n",
    "\n",
    "#动态调整学习率\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
    "\n",
    "# 训练网络\n",
    "def train(epoch, log_interval=128):\n",
    "    # Set model to training model\n",
    "    net.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        \n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.data.item()))\n",
    "# 验证网络\n",
    "def validate(loss_vector, accuracy_vector):\n",
    "    net.eval()\n",
    "    val_loss, correct = 0, 0\n",
    "    for data, target in test_loader:\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        output = net(data)\n",
    "        val_loss += criterion(output, target).data.item()\n",
    "        pred = output.data.max(1)[1]\n",
    "        correct += pred.eq(target.data).cpu().sum()\n",
    "    \n",
    "    val_loss /= len(test_loader)\n",
    "    loss_vector.append(val_loss)\n",
    "    \n",
    "    accuracy = 100. * correct.to(torch.float32) / len(test_loader.dataset)\n",
    "    accuracy_vector.append(accuracy)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.04f}%)\\n'.format(val_loss, correct, len(test_loader.dataset), accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d7a80d9",
   "metadata": {},
   "source": [
    "## main函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47e548b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/50000 (0%)]\tLoss: 4.736253\n",
      "Train Epoch: 1 [16384/50000 (33%)]\tLoss: 3.785982\n",
      "Train Epoch: 1 [32768/50000 (65%)]\tLoss: 3.890283\n",
      "Train Epoch: 1 [49152/50000 (98%)]\tLoss: 3.401800\n",
      "\n",
      "Validation set: Average loss: 3.4289, Accuracy: 1845/10000 (18.4500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLoss: 3.202366\n",
      "Train Epoch: 2 [16384/50000 (33%)]\tLoss: 3.500243\n",
      "Train Epoch: 2 [32768/50000 (65%)]\tLoss: 3.354596\n",
      "Train Epoch: 2 [49152/50000 (98%)]\tLoss: 3.163889\n",
      "\n",
      "Validation set: Average loss: 3.0637, Accuracy: 2433/10000 (24.3300%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLoss: 2.972501\n",
      "Train Epoch: 3 [16384/50000 (33%)]\tLoss: 2.850490\n",
      "Train Epoch: 3 [32768/50000 (65%)]\tLoss: 2.883779\n",
      "Train Epoch: 3 [49152/50000 (98%)]\tLoss: 2.621898\n",
      "\n",
      "Validation set: Average loss: 2.8177, Accuracy: 2933/10000 (29.3300%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLoss: 2.775138\n",
      "Train Epoch: 4 [16384/50000 (33%)]\tLoss: 2.706368\n",
      "Train Epoch: 4 [32768/50000 (65%)]\tLoss: 2.758733\n",
      "Train Epoch: 4 [49152/50000 (98%)]\tLoss: 2.512971\n",
      "\n",
      "Validation set: Average loss: 2.6420, Accuracy: 3297/10000 (32.9700%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLoss: 2.468033\n",
      "Train Epoch: 5 [16384/50000 (33%)]\tLoss: 2.566508\n",
      "Train Epoch: 5 [32768/50000 (65%)]\tLoss: 2.728972\n",
      "Train Epoch: 5 [49152/50000 (98%)]\tLoss: 2.378551\n",
      "\n",
      "Validation set: Average loss: 2.5151, Accuracy: 3566/10000 (35.6600%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLoss: 2.347323\n",
      "Train Epoch: 6 [16384/50000 (33%)]\tLoss: 2.452500\n",
      "Train Epoch: 6 [32768/50000 (65%)]\tLoss: 2.463754\n",
      "Train Epoch: 6 [49152/50000 (98%)]\tLoss: 2.422125\n",
      "\n",
      "Validation set: Average loss: 2.3633, Accuracy: 3844/10000 (38.4400%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLoss: 2.361147\n",
      "Train Epoch: 7 [16384/50000 (33%)]\tLoss: 2.387050\n",
      "Train Epoch: 7 [32768/50000 (65%)]\tLoss: 2.465285\n",
      "Train Epoch: 7 [49152/50000 (98%)]\tLoss: 2.131115\n",
      "\n",
      "Validation set: Average loss: 2.2872, Accuracy: 4001/10000 (40.0100%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLoss: 2.109146\n",
      "Train Epoch: 8 [16384/50000 (33%)]\tLoss: 2.139757\n",
      "Train Epoch: 8 [32768/50000 (65%)]\tLoss: 2.131537\n",
      "Train Epoch: 8 [49152/50000 (98%)]\tLoss: 2.088088\n",
      "\n",
      "Validation set: Average loss: 2.2191, Accuracy: 4155/10000 (41.5500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLoss: 2.010366\n",
      "Train Epoch: 9 [16384/50000 (33%)]\tLoss: 1.986696\n",
      "Train Epoch: 9 [32768/50000 (65%)]\tLoss: 2.030242\n",
      "Train Epoch: 9 [49152/50000 (98%)]\tLoss: 1.881004\n",
      "\n",
      "Validation set: Average loss: 2.1951, Accuracy: 4308/10000 (43.0800%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.01\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLoss: 1.821307\n",
      "Train Epoch: 10 [16384/50000 (33%)]\tLoss: 2.026515\n",
      "Train Epoch: 10 [32768/50000 (65%)]\tLoss: 1.973668\n",
      "Train Epoch: 10 [49152/50000 (98%)]\tLoss: 1.812714\n",
      "\n",
      "Validation set: Average loss: 2.1741, Accuracy: 4376/10000 (43.7600%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLoss: 1.657443\n",
      "Train Epoch: 11 [16384/50000 (33%)]\tLoss: 1.651994\n",
      "Train Epoch: 11 [32768/50000 (65%)]\tLoss: 1.312347\n",
      "Train Epoch: 11 [49152/50000 (98%)]\tLoss: 1.529799\n",
      "\n",
      "Validation set: Average loss: 1.9424, Accuracy: 4957/10000 (49.5700%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLoss: 1.832086\n",
      "Train Epoch: 12 [16384/50000 (33%)]\tLoss: 1.489489\n",
      "Train Epoch: 12 [32768/50000 (65%)]\tLoss: 1.375931\n",
      "Train Epoch: 12 [49152/50000 (98%)]\tLoss: 1.503435\n",
      "\n",
      "Validation set: Average loss: 1.9413, Accuracy: 5012/10000 (50.1200%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLoss: 1.315036\n",
      "Train Epoch: 13 [16384/50000 (33%)]\tLoss: 1.606793\n",
      "Train Epoch: 13 [32768/50000 (65%)]\tLoss: 1.439081\n",
      "Train Epoch: 13 [49152/50000 (98%)]\tLoss: 1.393943\n",
      "\n",
      "Validation set: Average loss: 1.9335, Accuracy: 5036/10000 (50.3600%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLoss: 1.286537\n",
      "Train Epoch: 14 [16384/50000 (33%)]\tLoss: 1.435720\n",
      "Train Epoch: 14 [32768/50000 (65%)]\tLoss: 1.248281\n",
      "Train Epoch: 14 [49152/50000 (98%)]\tLoss: 1.323145\n",
      "\n",
      "Validation set: Average loss: 1.9360, Accuracy: 5033/10000 (50.3300%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLoss: 1.338856\n",
      "Train Epoch: 15 [16384/50000 (33%)]\tLoss: 1.298447\n",
      "Train Epoch: 15 [32768/50000 (65%)]\tLoss: 1.601942\n",
      "Train Epoch: 15 [49152/50000 (98%)]\tLoss: 1.425462\n",
      "\n",
      "Validation set: Average loss: 1.9348, Accuracy: 5046/10000 (50.4600%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLoss: 1.261127\n",
      "Train Epoch: 16 [16384/50000 (33%)]\tLoss: 1.090739\n",
      "Train Epoch: 16 [32768/50000 (65%)]\tLoss: 1.124883\n",
      "Train Epoch: 16 [49152/50000 (98%)]\tLoss: 1.347044\n",
      "\n",
      "Validation set: Average loss: 1.9240, Accuracy: 5086/10000 (50.8600%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLoss: 1.024868\n",
      "Train Epoch: 17 [16384/50000 (33%)]\tLoss: 1.156864\n",
      "Train Epoch: 17 [32768/50000 (65%)]\tLoss: 1.124341\n",
      "Train Epoch: 17 [49152/50000 (98%)]\tLoss: 1.224529\n",
      "\n",
      "Validation set: Average loss: 1.9261, Accuracy: 5093/10000 (50.9300%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLoss: 0.973785\n",
      "Train Epoch: 18 [16384/50000 (33%)]\tLoss: 1.307688\n",
      "Train Epoch: 18 [32768/50000 (65%)]\tLoss: 1.101220\n",
      "Train Epoch: 18 [49152/50000 (98%)]\tLoss: 1.132491\n",
      "\n",
      "Validation set: Average loss: 1.9281, Accuracy: 5095/10000 (50.9500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLoss: 1.277976\n",
      "Train Epoch: 19 [16384/50000 (33%)]\tLoss: 1.494433\n",
      "Train Epoch: 19 [32768/50000 (65%)]\tLoss: 1.032376\n",
      "Train Epoch: 19 [49152/50000 (98%)]\tLoss: 0.912083\n",
      "\n",
      "Validation set: Average loss: 1.9301, Accuracy: 5119/10000 (51.1900%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLoss: 1.075853\n",
      "Train Epoch: 20 [16384/50000 (33%)]\tLoss: 1.295037\n",
      "Train Epoch: 20 [32768/50000 (65%)]\tLoss: 1.245921\n",
      "Train Epoch: 20 [49152/50000 (98%)]\tLoss: 1.182334\n",
      "\n",
      "Validation set: Average loss: 1.9148, Accuracy: 5155/10000 (51.5500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 21 [0/50000 (0%)]\tLoss: 1.273001\n",
      "Train Epoch: 21 [16384/50000 (33%)]\tLoss: 0.959484\n",
      "Train Epoch: 21 [32768/50000 (65%)]\tLoss: 0.910945\n",
      "Train Epoch: 21 [49152/50000 (98%)]\tLoss: 1.096905\n",
      "\n",
      "Validation set: Average loss: 1.8972, Accuracy: 5197/10000 (51.9700%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 22 [0/50000 (0%)]\tLoss: 1.129305\n",
      "Train Epoch: 22 [16384/50000 (33%)]\tLoss: 1.058514\n",
      "Train Epoch: 22 [32768/50000 (65%)]\tLoss: 1.081863\n",
      "Train Epoch: 22 [49152/50000 (98%)]\tLoss: 1.134780\n",
      "\n",
      "Validation set: Average loss: 1.8989, Accuracy: 5190/10000 (51.9000%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 23 [0/50000 (0%)]\tLoss: 0.925614\n",
      "Train Epoch: 23 [16384/50000 (33%)]\tLoss: 0.991496\n",
      "Train Epoch: 23 [32768/50000 (65%)]\tLoss: 1.051487\n",
      "Train Epoch: 23 [49152/50000 (98%)]\tLoss: 0.902400\n",
      "\n",
      "Validation set: Average loss: 1.8971, Accuracy: 5192/10000 (51.9200%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 24 [0/50000 (0%)]\tLoss: 0.800114\n",
      "Train Epoch: 24 [16384/50000 (33%)]\tLoss: 0.914387\n",
      "Train Epoch: 24 [32768/50000 (65%)]\tLoss: 1.117373\n",
      "Train Epoch: 24 [49152/50000 (98%)]\tLoss: 0.899736\n",
      "\n",
      "Validation set: Average loss: 1.8974, Accuracy: 5195/10000 (51.9500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 25 [0/50000 (0%)]\tLoss: 0.838340\n",
      "Train Epoch: 25 [16384/50000 (33%)]\tLoss: 0.876135\n",
      "Train Epoch: 25 [32768/50000 (65%)]\tLoss: 1.063383\n",
      "Train Epoch: 25 [49152/50000 (98%)]\tLoss: 1.039163\n",
      "\n",
      "Validation set: Average loss: 1.8985, Accuracy: 5200/10000 (52.0000%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 26 [0/50000 (0%)]\tLoss: 1.182668\n",
      "Train Epoch: 26 [16384/50000 (33%)]\tLoss: 1.032505\n",
      "Train Epoch: 26 [32768/50000 (65%)]\tLoss: 0.829930\n",
      "Train Epoch: 26 [49152/50000 (98%)]\tLoss: 1.075425\n",
      "\n",
      "Validation set: Average loss: 1.8994, Accuracy: 5175/10000 (51.7500%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 27 [0/50000 (0%)]\tLoss: 0.970844\n",
      "Train Epoch: 27 [16384/50000 (33%)]\tLoss: 1.125600\n",
      "Train Epoch: 27 [32768/50000 (65%)]\tLoss: 1.076636\n",
      "Train Epoch: 27 [49152/50000 (98%)]\tLoss: 0.855479\n",
      "\n",
      "Validation set: Average loss: 1.8958, Accuracy: 5197/10000 (51.9700%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 28 [0/50000 (0%)]\tLoss: 1.068520\n",
      "Train Epoch: 28 [16384/50000 (33%)]\tLoss: 0.970381\n",
      "Train Epoch: 28 [32768/50000 (65%)]\tLoss: 1.042760\n",
      "Train Epoch: 28 [49152/50000 (98%)]\tLoss: 1.114059\n",
      "\n",
      "Validation set: Average loss: 1.9002, Accuracy: 5209/10000 (52.0900%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 29 [0/50000 (0%)]\tLoss: 1.189543\n",
      "Train Epoch: 29 [16384/50000 (33%)]\tLoss: 0.965803\n",
      "Train Epoch: 29 [32768/50000 (65%)]\tLoss: 1.011646\n",
      "Train Epoch: 29 [49152/50000 (98%)]\tLoss: 1.128561\n",
      "\n",
      "Validation set: Average loss: 1.8987, Accuracy: 5219/10000 (52.1900%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 0.0001\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Train Epoch: 30 [0/50000 (0%)]\tLoss: 1.022503\n",
      "Train Epoch: 30 [16384/50000 (33%)]\tLoss: 1.120500\n",
      "Train Epoch: 30 [32768/50000 (65%)]\tLoss: 0.911759\n",
      "Train Epoch: 30 [49152/50000 (98%)]\tLoss: 0.987107\n",
      "\n",
      "Validation set: Average loss: 1.8981, Accuracy: 5200/10000 (52.0000%)\n",
      "\n",
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    initial_lr: 0.01\n",
      "    lr: 1e-05\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0.0005\n",
      ")\n",
      "Finished Training\n",
      "训练损失:1.8981273859739303\n",
      "训练精确度:52.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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1O+d6K0+EByCJ7551GNU79vLA3PVRh+Oc6yKeCDtwyoRBHDd2IP/53CqvgexcL+WJsANScK6wZncDv5+zLupwnHNdwBNhGo4fV85HJg7iv154h7qG5qjDcc5lmCfCNH33rMPZXt/ILY8vjToU51yGeSJM05TKMr5x6ngemLeepxdtijoc51wGeSLshGvPPIwpI/tz/aOL2LRzb9ThOOcyxBNhJ+THY9w+cxpNiSTXPfCmP4fsXC/hibCTxg4q4cbzj+bV1dv4pRd7cq5X8ER4ED5bNZJPHDOMnzyznEUbdkYdjnPuEHkiPAiS+JeLjmFw30K+ff8b1Df6LTXO9WSeCA9SWZ8Cbr1kCmu27eGWJ/yWGud6Mk+Eh+Ck8YP42kfHc9/r6/nzYh/N2rmeyhPhIbru44dxTEV/rn/0Ld7buS/qcJxzByFTdY1PlbQzpbbxD1KWnSNpuaRVkq7P9BeIWkFejNtnTqWhKcl3HlroBZ+c64EyUtc49FJLbWMzuxlAUhz4OUHhpqOAy9p5b482fnApN5x/FH9btY17XvZbapzrabqirnGq44BVZrbazBqB+4ELDzbYbHbpjErOPnooP3lmBau31EUdjnOuEzJZ1/hESW9KelrS0WFbBZA6oukG0k+iPYokbvnUJAryYtz0+FLMvIvsXE+RqbrGC4DRZjYF+Bnwx84Gki11jQ/FkL5FXHvmRF5csYXZSzdHHY5zLk0ZqWtsZrvMrC6cfgrIlzQIqAYqU1YdGbZ9SDbVNT4UV5w0hsOGlnLzE0t9RGvneoiM1DWWNCxcD0nHhdvdBswFJkoaK6mAoAD8rEwFn43y4zFuumASG7bv5c4X3ok6HOdcGjJV1/hi4OuSmoG9wEwLTpI1S7oGeAaIA782syWZ/QrZ58Tx5Zw/ZQR3vvgOn5k+klHlfaIOyTl3AMrGk/pVVVU2b968qMM4JJt27uWMW1/kpPGDuOeKqqjDcc4Bkuab2Yf+IP3Jki4yvH8x3zpjIn9Ztpnn366JOhzn3AF4IuxCf3fyWMYNLuGmx5d4gXjnspgnwi5UkBfjpguOZu22eu55aU3U4Tjn2uGJsIt9ZOJgzp00jJ89t5LqHV7nxLls5ImwG/y/84LHq3/4pI9b6Fw28kTYDSrKirnmtAk8teg9Xl65NepwnHOteCLsJl/9yDhGl/fhhlmLaWxORh2Ocy6FJ8JuUpQf58bzj+adLXu460V/4sS5bOKJsBuddsQQPjl5OLfNXsGPnn7bB3F1Lkuk84idy6DbL51KWXE+d734Dqu31HH7zKn0KfBfg3NR8iPCbpYfj/HPn5rEDecfxV+WbebiO19l006/rca5KHkijIAkvnzyWH71pRm8W1vPhf/5N95cvyPqsJzLWZ4II3Ta4UN45OsnUZAX45JfvMqTb22KOiTncpInwogdPqwvf7z6ZCZV9Ofqexfws7+u9GH+netmngizwKDSQv7w1eO5aFoFt85ewd8/sNBHt3auG/nlyixRlB/ntkumMGFIKT9+ZjmrttTx889NZ3R5SdShOdfrZarA++clvSVpkaRXJE1JWbY2bF8oqWePttrFJHH1aRO454tVrK/dy3l3vMzTi/y8oXNdLVMF3tcAHzOzY4BbgLtbLT8tLPzuQzWn4cyjhvLEN09h3JBSvv6HBdw4y8czdK4rZaTAu5m9Ymbbw9k5BNXq3CGoHNiHh752In938lh+88paPnvXq6yvrY86LOd6pUwWeG/xFeDplHkDnpU0X9KVB9h2j69rnGkFeTF+cP5R3HX5sazZuodP3vESzyx5L+qwnOt1MlXgvWWd0wgS4fdSmk8xs+nAuQTd6o+29d7eUte4K5wzaRhPfvMjjC4v4Wv/PZ9bnljqI9g4l0EZKfAerjMZuAe40My2tbSbWXX4swZ4DDjuUIPORaPK+/Dw10/kihNH86uX1/DZu17hgbnvsmG7d5edO1Qd3j6TZoH3UcCjwBfMbEVKewkQM7Pd4fRZwM0ZiTwHFebFuenCSRw/rpybHl/C9x5ZBMCY8j6cPGEQp0wYxInjyynrUxBxpM71LB3WNZZ0CvASsAho6Y99oMC7pHuAzwDrwuXNZlYlaRzBUSAESfdeM/thR0H1hrrGXc3MWFlTx8srt/K3VVuZs3obexoTSHBMRX9OnjCIk8aXM23UAEoL/XZR56D9usZe4L2XaEokeXP9Dl5eFSTGN97dQXPSiAmOGtGPqtEDqRozgKrRAxnWvyjqcJ2LhCfCHFPX0Mz8dduZv7aWuWu3s3D9DvaGj+1VlBUzY8wAjh0zkE8eM5yBJd6VdrnBE2GOa0okWbZpF3PXbmf+uiA5btndwPRRZTz6jZOjDs+5btFeIvSTRzkiPx5j8sgyJo8s4yunjMXMuPt/VvOvT7/Nsk27OHJ4v6hDdC4yPvpMjpLEJVWVFMRjPDB3fdThOBcpT4Q5bEBJAWdPGsZjb1T7sF8up3kizHEzZ1Syc2+TP7rncponwhx34rhyKgcWe/fY5TRPhDkuFhOXVlXyyjvbWLdtT9ThOBcJT4SOi4+tJCZ4cJ4fFbrc5InQMax/EacePoSH5m2gOeGj2rjc44nQAXDpjEpqdjfw4gofC9LlHk+EDoDTjxjCoNJC7veLJi4HeSJ0QPDkycXHjuS5t2uo2bUv6nCc61aeCN1+l86oJJE0Hl6wIepQnOtWngjdfmMHlXD82IE8MHc92TgYh3NdJVN1jSXpDkmrwvrG01OWXSFpZfi6ItNfwGXWzOMqWbetnjmra6MOxbluk6m6xucCE8PXlcCdAJIGAjcAxxPUKrlB0oAMxe66wLmThtO3KI8H5r4bdSjOdZuM1DUGLgR+Z4E5QJmk4cDZwGwzqw3rHs8GzsnoN3AZVZQf56JpFTy1+D121jdFHY5z3SJTdY0rgNT7LjaEbe21t7Vtr2ucJS6pqqSxOcmf3qyOOhTnukVG6xofCq9rnD0mVfRnUkU/7nvdL5q43JCpusbVQGXK/Miwrb12l+UunTGKZZt2sbg64//znMs66Vw17rCuMTAL+GJ49fgEYKeZbQKeAc6SNCC8SHJW2Oay3AVTRlCUH+N+v2jickA6R4QnA18ATpe0MHx9QtJVkq4K13kKWA2sAn4JfAPAzGqBW4C54evmsM1luf7F+XzimOHMWriR+sbmqMNxrkt5FTvXrtfX1HLJL15lWL8iigvixATxmIgpeMVjIhYTBXExoE8B5aWFDCotoLykZTqcLy2ktDCPxkSSxuaUVyLBvqbk/nazYPv7X0qZjkE8FmNQaQF9i/Kj3jWuh/Iqdq7TZowZwDWnTWDD9noSBsmkkUgaCTOSSSNpRsKgsTnBum31LHh3O7V7Gkl28f/WfkV5jCgrZuSAYkaUFVNRVkxFOD20XxHJpNGcNJrCBNuUSNKUCOcTSZrD6ZblH1zXaG5ZljLdlDSamt+fTiSM4WVFHD60LxOH9uWwoaWeoHswT4SuXZL47tmHd+o9iaSxo76RrXWNbKtrYOue4OeehmYK8mIUxGMU5MWD6bwYhS0/4zEQJJOQMCORTJJIBttrSb7NiSQ1uxvYuGMv1dv3smH7Xl5bU8vufV3TdS+Ix8iPi7x4jPx4jIL908ER8YsrtrA3pejViP5FHDasL4cNDV5BkiylKD/eJfG5zPFE6DIqHhPlpYWUlxYCfbvlM3fta9qfHLfsbgi760Hyyo+L/DAB58WC6fxYkHzz4h9cLy8erhcXeTERXCdsXzJpbNi+l+Wbd7Ni/6uOV1ZtozEc4DYvJiYMKQ1uSRrRj0kV/TlyeD9KCv1PL5v4OULnMqw5kWRdbT1vb9rN0k07WVy9iyUbd7K1rhEAKRjg4ugR/Rk1sJhEMnhPSxc9kbSgi54MuvEI8mMiPx7bf0SaF4uRnyfyY0HiBjADCyds/7ztb089tdFypJ3cPw1Je3/ejP3Lg1cwnx8XxQV5FOfHKM6Ph9NxiguC+cL8OA3NSeobmtnT0ExdQ4L6xmbqwvk9DQn2NSeIKfhnkxd+l3hM5MeD88Et8zEFvRIJYhIi+BmLAYhplWVcMqOyzd9Be/wcoXPdJC8eY/zgUsYPLuWTk4cDYGbU7G5gcfVOlmzcxeLqnSxYt50n39pIXuz9o9C8liPXeJAM8uICg6Zky7nNIEE2NQfnKpsTyTbPyUogwkQSzsc+cPEpuBgVS7koFYsRtIXtsfA9+y+MCZoSxr6mBHubEtQ3Bj8bm9sv71BSEKekMC98xSkpyGNAn4L9Cbc5aextSuz/R9DSFiTjIAEnLUzmKfNJC462O5sI2/2dZWQrzrkDksTQfkUM7VfEGUcOzei2k2EmVHgE1d0SYTLb25hgX1OCwrwYJYXBkWIs1v3xHAxPhM71cFEnm3hMlBbmUdqDz3v6wKzOuZznidA5l/M8ETrncp4nQudczvNE6JzLeVl5Q7WkLcC6lKZBwNaIwumsnhQreLxdqSfFCrkR72gz+9DIz1mZCFuTNK+tu8GzUU+KFTzertSTYoXcjte7xs65nOeJ0DmX83pKIrw76gA6oSfFCh5vV+pJsUIOx9sjzhE651xX6ilHhM4512WyOhFKOkfSckmrJF0fdTwdkbRW0qKwwFXWDago6deSaiQtTmkbKGm2pJXhzwFRxtiinVhvlFSdWkQsyhhTSaqU9LykpZKWSPp22J6t+7e9eLNuH0sqkvS6pDfDWG8K28dKei3MDw9IKjjoz8jWrrGkOLAC+DiwgaAK3mVmtjTSwA5A0lqgysyy8l4sSR8F6oDfmdmksO3fgVoz+1H4z2aAmX0vyjjDuNqK9Uagzsx+EmVsbZE0HBhuZgsk9QXmA58CvkR27t/24r2ELNvHYUnhEjOrC2usvwx8G7gOeNTM7pd0F/Cmmd15MJ+RzUeExwGrzGy1mTUC9wMXRhxTj2Zm/wO0Lqd6IfDbcPq3BH8MkWsn1qxlZpvMbEE4vRtYBlSQvfu3vXizjgXqwtn88GXA6cDDYfsh7dtsToQVwPqU+Q1k6S8qhQHPSpov6cqog0nTUDPbFE6/B2R21NDMu0bSW2HXOSu6ma1JGgNMA16jB+zfVvFCFu5jSXFJC4EaYDbwDrDDzFoqdx1SfsjmRNgTnWJm04FzgavD7l2PYcF5kuw8VxK4ExgPTAU2AbdGGk0bJJUCjwDXmtmu1GXZuH/biDcr97GZJcxsKjCSoLd4RCa3n82JsBpILUgwMmzLWmZWHf6sAR4j+IVlu83h+aKW80Y1EcfTLjPbHP5BJIFfkmX7Nzx/9QjwBzN7NGzO2v3bVrzZvo/NbAfwPHAiUCapZVjsQ8oP2ZwI5wITwytDBcBMYFbEMbVLUkl40hlJJcBZwOIDvysrzAKuCKevAP4UYSwH1JJQQheRRfs3PKH/K2CZmd2Wsigr92978WbjPpY0WFJZOF1McAF1GUFCvDhc7ZD2bdZeNQYIL93fDsSBX5vZD6ONqH2SxhEcBUJQC+bebItX0n3AqQSjdmwGbgD+CDwIjCIY8ecSM4v8IkU7sZ5K0GUzYC3wtZTzb5GSdArwErAIaCnr9o8E592ycf+2F+9lZNk+ljSZ4GJInODg7UEzuzn8m7sfGAi8AVxuZg0H9RnZnAidc647ZHPX2DnnuoUnQudczvNE6JzLeZ4InXM5zxOhcy7neSJ0zuU8T4TOuZznidA5l/P+FxI5QR02qfWgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练网络、打印训练过程\n",
    "if __name__ == \"__main__\":\n",
    "    lossv, accv = [], []\n",
    "    for epoch in range(1, epochs + 1):\n",
    "        train(epoch)\n",
    "        validate(lossv, accv)\n",
    "        scheduler.step()\n",
    "        print(optimizer)\n",
    "\n",
    "    print('Finished Training')\n",
    "\n",
    "    # 绘制训练损失、精确度\n",
    "    print(\"训练损失:{}\\n训练精确度:{}%\".format(lossv[-1], accv[-1].item()))\n",
    "    plt.figure(figsize=(5,3))\n",
    "    plt.plot(np.arange(1,epochs+1), lossv)\n",
    "    plt.title('validation loss')\n",
    "    plt.savefig('figs/SKnet20_loss.png')\n",
    "\n",
    "    plt.figure(figsize=(5,3))\n",
    "    plt.plot(np.arange(1,epochs+1), accv)\n",
    "    plt.title('validation accuracy')\n",
    "    plt.savefig('figs/SKnet20_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4995fc02",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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